Survival prediction of heart failure patients using motion-based analysis method

Guo, Saidi; Zhang, Heye; Gao, Yifeng; Wang, Hui; Xu, Lei; Gao, Zhifan; Guzzo, Antonella; Fortino, Giancarlo

doi:10.1016/j.cmpb.2023.107547

Cited by 13 publications

(2 citation statements)

References 53 publications

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“…A fusion strategy proposed is based on the point-wise mutual information (PMI) and preserves salience features [ 35 ]. Moreover, the proposed strategy keeps spatial consistency for a fusion image.…”

Section: Related Studiesmentioning

confidence: 99%

Detection method of organic light-emitting diodes based on small sample deep learning

Qiu,

Huang,

Feng

et al. 2024

PLoS ONE

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In order to solve the surface detection problems of low accuracy, low precision and inability to automate in the production process of late-model display panels, a little sample-based deep learning organic light-emitting diodes detection model SmartMuraDetection is proposed. First, aiming at the detection difficulty of low surface defect contrast, a gradient boundary enhancement algorithm module is designed to automatically identify and enhance defects and background gray difference. Then, the problem of insufficient little sample data sets is solved, Poisson fusion image enhancement module is designed for sample enhancement. Then, a TinyDetection model adapted to small-scale target detection is constructed to improve the detection accuracy of defects in small-scale targets. Finally, SEMUMaxMin quantization module is proposed as a post-processing module for the result images derived from network model reasoning, and accurate defect data is obtained by setting a threshold filter. The number of sample images in the experiment is 334. This study utilizes an organic light-emitting diodes detection model. The detection accuracy of surface defects can be improved by 85% compared with the traditional algorithm. The method in this paper is used for mass production evaluation in the actual display panel production site. The detection accuracy of surface defects reaches 96%, which can meet the mass production level of the detection equipment in this process section.

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Section: Related Studiesmentioning

confidence: 99%

Detection method of organic light-emitting diodes based on small sample deep learning

Qiu,

Huang,

Feng

et al. 2024

PLoS ONE

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show abstract

“…[11][12][13] The popular machine learning algorithms in AI have strong tting abilities. 14 They learn patterns and rules from data and use them to predict new data. With a signicant quantity of labeled data, machine learning algorithms can learn hidden patterns and complex relationships in the data and even capture patterns that are difficult for humans to perceive.…”

Section: Introductionmentioning

confidence: 99%

TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining

Lv¹,

Chen²,

He³

et al. 2023

Chem. Sci.

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Machine Learning for Time-to-Event Prediction and Survival Clustering: A Review from Statistics to Deep Neural Networks

Luo,

Xie,

Yang

et al. 2024

Communications in Computer and Information Science

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Survival prediction of heart failure patients using motion-based analysis method

Cited by 13 publications

References 53 publications

Detection method of organic light-emitting diodes based on small sample deep learning

Detection method of organic light-emitting diodes based on small sample deep learning

TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining

Machine Learning for Time-to-Event Prediction and Survival Clustering: A Review from Statistics to Deep Neural Networks

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